Publication

Efficient Image Reconstruction Under Sparsity Constraints with Application to MRI and Bioluminescence Tomography

Related publications (45)

Variational Methods For Continuous-Domain Inverse Problems: the Quest for the Sparsest Solution

The goal of this thesis is to study continuous-domain inverse problems for the reconstruction of sparse signals and to develop efficient algorithms to solve such problems computationally. The task is to recover a signal of interest as a continuous function ...

EPFL2022

Automated Design: A Journey Across Modelling, Optimization, and Education

Cyril Picard

Machine intelligence greatly impacts almost all domains of our societies. It is profoundly changing the field of mechanical engineering with new technical possibilities and processes. The education of future engineers also needs to adapt in terms of techni ...

EPFL2021

Multikernel Regression with Sparsity Constraint

Michaël Unser, Shayan Aziznejad

In this paper, we provide a Banach-space formulation of supervised learning with generalized total-variation (gTV) regularization. We identify the class of kernel functions that are admissible in this framework. Then, we propose a variation of supervised l ...

2021

The electric autonomous dial-a-ride problem

Claudia Bongiovanni

This thesis develops mathematical programming frameworks to operate electric autonomous vehicles in the context of ride-sharing services. The introduced problem is a novel variant of the Dial-a-Ride Problem (DARP), denoted by the electric Autonomous Dial-a ...

EPFL2020

Evolutionary Algorithm Optimization of Staggered Biological or Biomimetic Composites Using the Random Fuse Model

Roberto Guarino, Gianluca Costagliola, Federico Bosia

In Nature, biological materials such as nacre, bone, and dentin display an enhanced mechanical strength due to their structure characterized by hard inclusions embedded in a soft matrix. This structure has inspired the design of artificial materials with o ...

2020

Exploiting structure of chance constrained programs via submodularity

Maryam Kamgarpour, Tony Alan Wood

We introduce a novel approach to reduce the computational effort of solving convex chance constrained programs through the scenario approach. Instead of reducing the number of required scenarios, we directly minimize the computational cost of the scenario ...

2019

The Minimization of Public Facilities With Enhanced Genetic Algorithms Using War Elimination

Michael Mark

In this paper, we focus on the problem of minimizing a network of state facilities that provide essential public services (schools, offices, and hospitals). The goal is to reduce the size of the network in order to minimize the costs associated with it. Ho ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2019

A Comparison of Greedy and Global Searches for Measurement-System Design in Bridge Load Testing

Ian Smith, Numa Joy Bertola

Due to conservative design models and safe construction practices, infrastructure often has significant yet-unknown reserve capacity that greatly exceeds code requirements. Reserve-capacity assessments lead to better asset-management decisions through eith ...

2019

The influence of limited visual sensing on the Reynolds ﬂocking algorithm

Dario Floreano, Fabrizio Schiano, Enrica Soria

The interest in multi-drone systems flourished in the last decade and their application is promising in many fields. We believe that in order to make drone swarms flying smoothly and reliably in real-world scenarios we need a first intermediate step which ...

2019

Phaseless Diffraction Tomography With Regularized Beam Propagation

Michaël Unser, Demetri Psaltis, Thanh-An Michel Pham, Emmanuel Emilien Louis Soubies, Ferréol Arnaud Marie Soulez, Alexandre Goy, Joowon Lim

In recent years, researchers have obtained impressive reconstructions of the refractive index (RI) of biological objects through the combined use of advanced physics (nonlinear forward model) and regularization. Here, we propose an adaptation of these tech ...

IEEE2018

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